Governed AI footprint
326
AI tools, workflows, models, and vendors under named financial-services ownership
Primary Market
Govern enterprise AI across financial services with Enterprise AI Firewall control and audit-defensible evidence.
SENTRUM gives financial institutions a unified Enterprise AI Firewall and AI governance layer across banking, insurance, and capital markets operating models. It helps executives, Risk, Compliance, Audit, and Technology teams control AI usage in real time, govern third-party AI, reduce Shadow AI, and demonstrate supervisory readiness with evidence rather than narrative.
Enterprise AI FirewallCross-sector governanceEvidence-led controlsRegulator-ready reporting
Runtime control coverage
89%
Mapped to policy, owner, obligations, and evidence
Escalated events
29
High-risk interactions flagged for supervisory review
Inspection readiness
41
Evidence packs ready for audit, regulators, and board committees
Sector focusFinancial Services
Operating modelFirewall + governance
Review postureBoard, audit, regulator
DeploymentPrivate / hybrid / on-prem
Operating fit
Built for financial-services groups under sustained scrutiny
- Cross-entity AI governance
- Third-party AI control
- Board and regulator reporting
- Evidence-backed supervisory operations
Sector reality
High-consequence decisions
Control posture
Group-wide governance
Deployment fit
Private to hybrid cloud
Scrutiny readiness
Board, audit, regulator
Why this industry is different
One supervisory architecture across multiple financial-services operating models
Financial-services firms need more than AI visibility. They need runtime control, accountable approvals, third-party governance, and evidence that stands up to audit and supervisory review across multiple regulated business lines.
Fragmented AI adoption
Different business units adopt AI at different speeds, leaving group-level leadership without one trusted control view.
Cross-entity policy drift
Oversight expectations rise when AI spans regulated entities, products, and jurisdictions with different control obligations.
Third-party concentration
Vendor AI, outsourced workflows, and embedded external models materially expand the risk surface unless onboarding and monitoring are controlled.
Supervisory challenge
Reporting claims fail when evidence lineage is weak, inconsistent across entities, or disconnected from runtime control actions.
Priority AI use cases
Structured workflows where governance must be operational, not aspirational.
SENTRUM supports regulated financial-services use cases where control, governance, and audit defensibility must scale across entities and lines of business.
01 · Employee AI productivity
Employee AI productivity
Control internal AI usage across operations, support, legal, compliance, and business teams with named ownership and runtime policy enforcement.
Financial Services workflow
Employee AI productivity Illustrative sector workflow panel representing governed runtime control, evidence capture, and supervisory readiness.02 · Cross-entity policy alignment
Cross-entity policy alignment
Harmonize governance standards across banking, insurance, and capital-markets entities while preserving local ownership and escalation.
Financial Services workflow
Cross-entity policy alignment Illustrative sector workflow panel representing governed runtime control, evidence capture, and supervisory readiness.03 · Vendor AI governance
Vendor AI governance
Govern vendor AI exposure, due diligence, obligations, and ongoing supervisory review through one cross-entity control model.
Financial Services workflow
Vendor AI governance Illustrative sector workflow panel representing governed runtime control, evidence capture, and supervisory readiness.04 · Supervisory reporting
Supervisory reporting
Package evidence-backed reporting for board committees, internal audit, risk, and supervisors without manual reconstruction.
Financial Services workflow
Supervisory reporting Illustrative sector workflow panel representing governed runtime control, evidence capture, and supervisory readiness.Risk and control model
Map sector risk to required control and expected evidence.
Risk themes
Required controls
Evidence expectations
Inventory fragmentation
Named owners, business mapping, risk tiering, and approval states
Exportable lineage, ownership history, and evidence linkage
Policy drift
Central policy model, obligations mapping, and exception workflow
Control attestations, approval trail, and remediation status
Vendor opacity
Due diligence workflow, evidence requirements, and periodic reviews
Vendor evidence packs, decision records, and review outcomes
How SENTRUM fits
Modules selected for this industry control model.
These modules are the highest-priority control capabilities for Financial Services organizations adopting AI under scrutiny.
AI Usage Visibility
Governed telemetry, ownership, and AI asset visibility.
Risk Scoring & Obligations
Convert AI findings into named risks, obligations, owners, and due dates.
Vendor AI Inventory
Track third-party AI services, dependencies, due diligence, and ongoing review.
Compliance Reports
Produce regulator-ready reporting with evidence-backed supervisory context.
Audit Evidence Packs
Assemble inspection-ready evidence packs without manual reconstruction.
Enterprise AI Firewall & Policy Enforcement
Apply preventive and detective control expectations consistently.
Operating stakeholders
Multi-buyer relevance for enterprise sales, governance, and implementation.
Group Risk
View AI risk tiering, exceptions, obligations, and remediation across entities and business units.
Compliance
Assess deployment governance, operating-model fit, and how SENTRUM supports group-level oversight with entity-level accountability.
Internal Audit
Review evidence lineage, control execution, and supervisory reporting artifacts with attributable, reconstructable records.
CIO / Transformation
Deploy one operating model for AI control without fragmenting governance across business lines.
Deployment and architecture fit
Executive governance layer for banking, insurance, and capital markets
SENTRUM supports enterprise deployment where business units differ, but governance expectations do not. It centralizes visibility, runtime control, evidence, and oversight while preserving local accountability.
Architecture notes
- Sub-sector overlays for banking, insurance, and capital markets
- Common control model with entity-specific accountability
- Evidence ledger and pack generation for inspections
Evidence and reporting
Designed for audit, executive review, and regulator-facing evidence requests.
Capture policy decisions, exceptions, approvals, and exportable evidence so group functions can respond consistently to internal audit, risk committees, and regulatory review.
FAQ
Decision-stage questions for deployment, control, and evidence.
Does it support regulator-facing evidence requests?
Yes. SENTRUM is built to assemble evidence lineage, approvals, and reporting outputs suitable for supervisory and audit challenge.
Can we deploy in private cloud or on-premises?
Yes. SENTRUM is designed to assemble evidence lineage, approvals, and reporting artifacts suitable for internal audit and supervisory challenge.
How do we handle different regulatory obligations by jurisdiction?
Yes. SENTRUM supports on-premises, private cloud, and hybrid deployment across regulated financial-services environments.
Next step
Take financial-services AI governance from fragmented oversight to controlled execution.
Discuss how SENTRUM can establish one Enterprise AI Firewall and supervisory model across regulated financial-services entities, workflows, and vendors.